Machine learning-based prediction and assessment of recent dynamics of forest net primary productivity in Romania.

While the analysis of spatio-temporal changes in the net primary productivity (NPP) of forests can provide critical information on carbon cycle and climate change, these ecological trends have remained unclear in many countries worldwide, including Romania. By using complex (satellite, forest and climate) data, many sophisticated (machine learning) algorithms and some widely applied (the Mann-Kendall test and Sen's slope estimator) statistical procedures, this study investigates, for the first time, recent forest NPP trends (1987-2018) that occurred in Romania, in relation to climate change that affected the country over the past decades. Following the modelling, mapping and assessment of NPP dynamics, results showed almost exclusively positive trends for this ecological parameter, which accounts for ∼99% of all forest NPP changes that occurred throughout the country, after 1987. Interestingly, almost three quarters (∼73%) of all NPP increasing trends are statistically significant, which indicates that Romania's forests have recently experienced a large-scale improvement in carbon fluxes and stocks. Investigations of eco-climatic relationships suggest that climate change has partially contributed to these surprising NPP dynamics observed in recent decades. All these findings can provide valuable information for forest management and for many stakeholders and policymakers who operate in the forestry and climate fields in Romania.

Global changes in soil organic carbon and implications for land degradation neutrality and climate stability.

Soil organic carbon (SOC) is a critical indicator for healthy and fertile lands across the world. It is also the planet's largest terrestrial carbon pool, so any changes of this pool may have profound implications for both land productivity and climate stability. However, SOC changes have so far remained largely unexplored, although their understanding is essential for many international environmental policies. Here we investigate for the first time recent global SOC changes, based on some SOC stock interannual data that were processed for the 2001-2015 period on a planetary scale. We analysed the global SOC dynamics using the Mann-Kendall test and Sen's slope estimator, which are widely acknowledged to be reliable geostatistical tools for detecting various environmental trends from global to local scale. We explored SOC changes via three metrics (averages, quantities, areas) of negative and positive trends, but also of the balance between soil carbon trends, a key statistic for monitoring land quality stability and soil-atmosphere carbon fluxes in the global environmental policies. Globally, we estimated a net average decrease of -58.6 t C km2 yr-1, a total loss of ~3.1 Pg C, and an area affected by net SOC losses of ~1.9 million km2. Using this triple statistic, we found that 79% of countries worldwide have been affected by net declines of SOC after 2001, which suggests that halting land degradation and mitigating climate change through the SOC pathway are still far from being achieved by international policies.

Arable lands under the pressure of multiple land degradation processes. A global perspective.

While agricultural systems are a major pillar in global food security, their productivity is currently threatened by many environmental issues triggered by anthropogenic climate change and human activities, such as land degradation. However, the planetary spatial footprint of land degradation processes on arable lands, which can be considered a major component of global agricultural systems, is still insufficiently well understood. This study analyzes the land degradation footprint on global arable lands, using complex geospatial data on certain major degradation processes, i.e. aridity, soil erosion, vegetation decline, soil salinization and soil organic carbon decline. By applying geostatistical techniques that are representative for identifying the incidence of the five land degradation processes in global arable lands, results showed that aridity is by far the largest singular pressure for these agricultural systems, affecting ~40% of the arable lands' area, which cover approximately 14 million km2 globally. It was found that soil erosion is another major degradation process, the unilateral impact of which affects ~20% of global arable systems. The results also showed that the two degradation processes simultaneously affect an additional ~7% of global arable lands, which makes this synergy the most common form of multiple pressure of land degradative conditions across the world's arable areas. The absolute statistical data showed that India, the United States, China, Brazil, Argentina, Russia and Australia are the most vulnerable countries in the world to the various pathways of arable land degradation. Also, in terms of percentages, statistical observations showed that African countries are the most heavily affected by arable system degradation. This study's findings can be useful for prioritizing agricultural management actions that can mitigate the negative effects of the two degradation processes or of others that currently affect many arable systems across the planet.

A unifying modelling of multiple land degradation pathways in Europe.

Land degradation is a complex socio-environmental threat, which generally occurs as multiple concurrent pathways that remain largely unexplored in Europe. Here we present an unprecedented analysis of land multi-degradation in 40 continental countries, using twelve dataset-based processes that were modelled as land degradation convergence and combination pathways in Europe's agricultural (and arable) environments. Using a Land Multi-degradation Index, we find that up to 27%, 35% and 22% of continental agricultural (~2 million km2) and arable (~1.1 million km2) lands are currently threatened by one, two, and three drivers of degradation, while 10-11% of pan-European agricultural/arable landscapes are cumulatively affected by four and at least five concurrent processes. We also explore the complex pattern of spatially interacting processes, emphasizing the major combinations of land degradation pathways across continental and national boundaries. Our results will enable policymakers to develop knowledge-based strategies for land degradation mitigation and other critical European sustainable development goals.